Discovering Differentially Expressed Genes in Yeast Stress Data

• Type: Article in International Conference Proceedings Book
• Date: 2014

Authors

Antonio Goncalves (Author) Other; Irene Ong (Author) Other; Jeffrey A Lewis (Author) Other; Vitor Santos Costa (Author) FCUP

Conference proceedings International

Title: 2014 IEEE 27TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS)

Pages: 537-538

27th IEEE International Symposium on Computer-Based Medical Systems (CBMS)

New York, NY, MAY 27-29, 2014

Indexing

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Scientific classification

FOS: Engineering and technology > Environmental biotechnology

Other information

Authenticus ID: P-009-VHP

DOI: 10.1109/cbms.2014.127

Abstract (EN): Transcriptional regulation plays an important role in every cellular decision. Gaining an understanding of the dynamics that govern how a cell will respond to diverse environmental cues is difficult using intuition alone. We try to discover how genes interact when submitted to stress by exploring techniques of gene expression data analysis. We use several types of data, including high-throughput data. These results will help us recreate plausible regulatory networks by using a probabilistic logical model. Hence, network hypotheses can be generated from existing gene expression data for use by experimental biologists.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 2